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Authors
# Name
1 Rodrigo Machado(rodrigo.machado@aluno.cefet-rj.br)
2 Lucas Tavares(lucas.giusti@eic.cefet-rj.br)
3 Lais Baroni(lais.baroni@eic.cefet-rj.br)
4 Fabio Porto (fporto@lncc.br)
5 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
6 Eduardo Ogasawara(eogasawara@ieee.org)

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Reference
# Reference
1 Anderson, R., Koh, Y. S., Dobbie, G., e Bifet, A. (2019). Recurring concept meta-learning for evolving data streams. Expert Systems with Applications, 138:112832.
2 Bayram, F., Ahmed, B. S., e Kassler, A. (2022). From concept drift to model degradation: An overview on performance-aware drift detectors. Knowledge-Based Systems, 245.
3 Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., e Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46.
4 Halstead, B., Koh, Y. S., Riddle, P., Pears, R., Pechenizkiy, M., e Bifet, A. (2021). Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency. Data Mining and Knowledge Discovery, 35:796 – 836. Iowa State University Mesonet (2026). FL ASOS Data Download. Technical report.
5 Kurian, J. F. e Allali, M. (2024). Detecting drifts in data streams using Kullback-Leibler (KL) divergence measure for data engineering applications. Journal of Data, Information and Management, 6:207 – 216.
6 Lu, S.-C., Song, W., Pfob, A., e Gibbons, C. (2025). Assessing the representativeness of large medical data using population stability index. BMC Medical Research Methodology, 25.
7 Misra, V. e Jayasankar, C. (2024). Advancing the Seasonal Outlook of the Wet Seasons of Florida. Weather and Forecasting, 39:1751 – 1759.
8 Ogasawara, E., Castro, A., Salles, D., Lima, J., Tavares, L., Carvalho, D., Bezerra, E., e Coutinho, R. (2025a). daltoolbox: Leveraging Experiment Lines to Data Analytics.
9 Ogasawara, E., Salles, R., Porto, F., e Pacitti, E. (2025b). Event Detection in Time Series. Synthesis Lectures on Data Management. Springer Nature Switzerland, Cham, 1 edition.
10 Ortíz Díaz, A., Del Campo-Ávila, J., Ramos-Jiménez, G., Frías Blanco, I., Caballero Mota, Y., Mustelier Hechavarría, A., e Morales-Bueno, R. (2015). Fast adapting ensemble: A new algorithm for mining data streams with concept drift. Scientific World Journal, 2015:235810.
11 Peeling, J. A., Judge, J., Misra, V., Jayasankar, C., e Lusher, W. R. (2023). Gap-free 16-year (2005–2020) sub-diurnal surface meteorological observations across Florida. Scientific Data, 10.
12 Peres, F. F. (2026). Effect sizes for nonparametric tests. Biochemia Medica, 36.
13 Read, J. e Zliobaite, I. (2025). Supervised Learning from Data Streams: An Overview and Update. ACM Computing Surveys, 57:1–37.
14 Sun, Y., Wang, Z., Bai, Y., Dai, H., e Nahavandi, S. (2018). A Classifier Graph Based Recurring Concept Detection and Prediction Approach. Computational Intelligence and Neuroscience, 2018:4276291.
15 Tavares, L., Carvalho, L., Machado, R., Carvalho, D., Pacitti, E., Porto, F., e Ogasawara, E. (2025). heimdall: Drift Adaptable Models.
16 Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., e Petitjean, F. (2016). Characterizing concept drift. Data Mining and Knowledge Discovery, 30:964 – 994.
17 Yurdakul, B. e Naranjo, J. (2020). Statistical properties of the population stability index. Journal of Risk Model Validation, 14:89 – 100.
18 Zhao, P., Cai, L.-W., e Zhou, Z.-H. (2020). Handling concept drift via model reuse. Machine Learning, 109:533 – 568.